Hi dear one,
I have paired end data and single end data (both are different single cell data), I want to find differential gene expression between them! Is this a good idea to convert paired end read to single end read? And how?
Thanks in advance! Looking for your help, I am new to this field!
No, as a general rule there's no need to convert your reads to single-end, aligners work just fine with paired-end data. The only exception is if you know a priori that the reads should overlap, in which can you can use flash or bbmap to overlap them to gain a small bit of speed (this tends to be more effort than its worth).
As said before, there is no reason to do that. If you want to do it to compare read counts from different experiments, you might want to rethink this.
Hi, I'm following up the previous post, but we a different question. We have samples from treatment A as pair-end reads and samples from treatment B as single-end reads. Can we take the first-pair end read file (R1) from trt A samples and perform diff. exp. analysis with trt B samples? Will this have any effect on the comparison between treatments?
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Why do you want it at the first place? There are tools which will fairly work on paired data.
I want to find the differentially expressed genes between two data sets ( one is paired end and another is single end)
As long as you remember to assign fragments to genes (with your paired-end data) rather than reads (default for single-end data) you should be okay to compare the two data sets.
If the only difference between the libraries is sequencing then you should be fine but I would just take read1 and ignore the other side, it might influence counting mapping to genes. However, I suspect that some other factors are different between these sets of libraries, e.g. fragmentation, selection, library generation kit and in this case your comparative analysis might find these biases instead of real biology.
Thank you Asaf........
thank you but sorry, I didn't understand your answer *to assign fragments to genes * , can you please explain this, @James ashmore
A single-end read comes from sequencing one end of a fragment. A paired-end reads comes from sequencing the same fragment twice (each end of the fragment is sequenced). With single-end data, a single read represents a single fragment, but with paired-end data two reads (each pair) represents a single fragment. When you count how many reads align to each gene you will on average get twice the read count using paired-end data then you would using single-end data.
Yes, thanks for your response